Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-el16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-el16 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-el16") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-el16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36ef9ac92f3af11711099ee073d5e869b4622d394c4f4cb85af89f6e5093763d
- Size of remote file:
- 368 MB
- SHA256:
- f4fd130b99616e2d56ce879c5e3e5272c63d1f310da35be125bd6e32c9da5ee7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.